Dec 12 2018

Conv1

see_comp_gradient('conv1', alpha, amount)

Conv2

see_comp_gradient('conv2', alpha, amount)

Dense

see_comp_gradient('fc1')

---------------------------------------------------------------------------
KeyboardInterrupt                         Traceback (most recent call last)
<ipython-input-14-fe6152487404> in <module>
----> 1 see_comp_gradient('fc1')

<ipython-input-10-04705655f328> in see_comp_gradient(layer)
     63 #             plt.savefig('{}/comp-with-label-q{}-{}-{}-{}.pdf'.format(dirpath, N, stdlabel, label, layer))
     64 #             plt.clf()
---> 65             plt.show()

/anaconda3/lib/python3.6/site-packages/matplotlib/pyplot.py in show(*args, **kw)
    251     """
    252     global _show
--> 253     return _show(*args, **kw)
    254 
    255 

/anaconda3/lib/python3.6/site-packages/ipykernel/pylab/backend_inline.py in show(close, block)
     37             display(
     38                 figure_manager.canvas.figure,
---> 39                 metadata=_fetch_figure_metadata(figure_manager.canvas.figure)
     40             )
     41     finally:

/anaconda3/lib/python3.6/site-packages/IPython/core/display.py in display(include, exclude, metadata, transient, display_id, *objs, **kwargs)
    296             publish_display_data(data=obj, metadata=metadata, **kwargs)
    297         else:
--> 298             format_dict, md_dict = format(obj, include=include, exclude=exclude)
    299             if not format_dict:
    300                 # nothing to display (e.g. _ipython_display_ took over)

/anaconda3/lib/python3.6/site-packages/IPython/core/formatters.py in format(self, obj, include, exclude)
    178             md = None
    179             try:
--> 180                 data = formatter(obj)
    181             except:
    182                 # FIXME: log the exception

<decorator-gen-9> in __call__(self, obj)

/anaconda3/lib/python3.6/site-packages/IPython/core/formatters.py in catch_format_error(method, self, *args, **kwargs)
    222     """show traceback on failed format call"""
    223     try:
--> 224         r = method(self, *args, **kwargs)
    225     except NotImplementedError:
    226         # don't warn on NotImplementedErrors

/anaconda3/lib/python3.6/site-packages/IPython/core/formatters.py in __call__(self, obj)
    339                 pass
    340             else:
--> 341                 return printer(obj)
    342             # Finally look for special method names
    343             method = get_real_method(obj, self.print_method)

/anaconda3/lib/python3.6/site-packages/IPython/core/pylabtools.py in <lambda>(fig)
    242 
    243     if 'png' in formats:
--> 244         png_formatter.for_type(Figure, lambda fig: print_figure(fig, 'png', **kwargs))
    245     if 'retina' in formats or 'png2x' in formats:
    246         png_formatter.for_type(Figure, lambda fig: retina_figure(fig, **kwargs))

/anaconda3/lib/python3.6/site-packages/IPython/core/pylabtools.py in print_figure(fig, fmt, bbox_inches, **kwargs)
    126 
    127     bytes_io = BytesIO()
--> 128     fig.canvas.print_figure(bytes_io, **kw)
    129     data = bytes_io.getvalue()
    130     if fmt == 'svg':

/anaconda3/lib/python3.6/site-packages/matplotlib/backend_bases.py in print_figure(self, filename, dpi, facecolor, edgecolor, orientation, format, bbox_inches, **kwargs)
   2051                     bbox_artists = kwargs.pop("bbox_extra_artists", None)
   2052                     bbox_inches = self.figure.get_tightbbox(renderer,
-> 2053                             bbox_extra_artists=bbox_artists)
   2054                     pad = kwargs.pop("pad_inches", None)
   2055                     if pad is None:

/anaconda3/lib/python3.6/site-packages/matplotlib/figure.py in get_tightbbox(self, renderer, bbox_extra_artists)
   2271 
   2272         for a in artists:
-> 2273             bbox = a.get_tightbbox(renderer)
   2274             if bbox is not None and (bbox.width != 0 or bbox.height != 0):
   2275                 bb.append(bbox)

/anaconda3/lib/python3.6/site-packages/matplotlib/axes/_base.py in get_tightbbox(self, renderer, call_axes_locator, bbox_extra_artists)
   4220 
   4221         for a in bbox_artists:
-> 4222             bbox = a.get_tightbbox(renderer)
   4223             if bbox is not None and (bbox.width != 0 or bbox.height != 0):
   4224                 bb.append(bbox)

/anaconda3/lib/python3.6/site-packages/matplotlib/axis.py in get_tightbbox(self, renderer)
   1140         ticks_to_draw = self._update_ticks(renderer)
   1141 
-> 1142         self._update_label_position(renderer)
   1143 
   1144         # go back to just this axis's tick labels

/anaconda3/lib/python3.6/site-packages/matplotlib/axis.py in _update_label_position(self, renderer)
   1932         # get bounding boxes for this axis and any siblings
   1933         # that have been set by `fig.align_xlabels()`
-> 1934         bboxes, bboxes2 = self._get_tick_boxes_siblings(renderer=renderer)
   1935 
   1936         x, y = self.label.get_position()

/anaconda3/lib/python3.6/site-packages/matplotlib/axis.py in _get_tick_boxes_siblings(self, renderer)
   1917         for nn, axx in enumerate(grp.get_siblings(self.axes)):
   1918             ticks_to_draw = axx.xaxis._update_ticks(renderer)
-> 1919             tlb, tlb2 = axx.xaxis._get_tick_bboxes(ticks_to_draw, renderer)
   1920             bboxes.extend(tlb)
   1921             bboxes2.extend(tlb2)

/anaconda3/lib/python3.6/site-packages/matplotlib/axis.py in _get_tick_bboxes(self, ticks, renderer)
   1123         for tick in ticks:
   1124             if tick.label1On and tick.label1.get_visible():
-> 1125                 extent = tick.label1.get_window_extent(renderer)
   1126                 ticklabelBoxes.append(extent)
   1127             if tick.label2On and tick.label2.get_visible():

/anaconda3/lib/python3.6/site-packages/matplotlib/text.py in get_window_extent(self, renderer, dpi)
    927             raise RuntimeError('Cannot get window extent w/o renderer')
    928 
--> 929         bbox, info, descent = self._get_layout(self._renderer)
    930         x, y = self.get_unitless_position()
    931         x, y = self.get_transform().transform_point((x, y))

/anaconda3/lib/python3.6/site-packages/matplotlib/text.py in _get_layout(self, renderer)
    337 
    338         # get the rotation matrix
--> 339         M = Affine2D().rotate_deg(self.get_rotation())
    340 
    341         offsetLayout = np.zeros((len(lines), 2))

/anaconda3/lib/python3.6/site-packages/matplotlib/transforms.py in rotate_deg(self, degrees)
   1987         and :meth:`scale`.
   1988         """
-> 1989         return self.rotate(np.deg2rad(degrees))
   1990 
   1991     def rotate_around(self, x, y, theta):

/anaconda3/lib/python3.6/site-packages/matplotlib/transforms.py in rotate(self, theta)
   1972         """
   1973         a = np.cos(theta)
-> 1974         b = np.sin(theta)
   1975         rotate_mtx = np.array([[a, -b, 0.0], [b, a, 0.0], [0.0, 0.0, 1.0]],
   1976                               float)

KeyboardInterrupt: 

import matplotlib.patches as mpatches
    
colors = ['#FF5733', '#FF3633', '#93FF33', '#3399FF', '#33FF80', '#7133FF', '#FFF033', '#FF33A8', '#FF3368', '#EF97F3']

alpha = 0.6
amount = 0.01

def see_comp_gradient(layer, alpha, amount):
    for stdlabel in range(10):
        
        # Sort the slice by standard label
        acts_label_std = act_Q_dict[stdlabel][layer]
        med_acts_label_std = acts_label_std[:, 1]
        sorted_slice = np.argsort(-np.array(med_acts_label_std)).astype(int)
        sorted_acts_std = np.array([acts_label_std[s] for s in sorted_slice])
        slices = list(range(len(sorted_slice)))
        
        for label in range(10):
            if label == stdlabel:
                continue
        
            # Activation of comparison layer
            layer_acts = act_Q_dict[label][layer]
            sorted_acts = np.array([layer_acts[s] for s in sorted_slice])

            # Draw half top ranges of std
            c1_std = colors[stdlabel]
            c2_std = lighten_color(c1_std, amount=amount)
            for i in range(int(N / 2)):
                color = gen_color(c1_std, c2_std, mix= 1 - (i * 2 / N))
                up_std, down_std = sorted_acts_std[:, i], sorted_acts_std[:, i + 1]
                plt.fill_between(slices, up_std, down_std, facecolor=color, alpha=alpha)

            # Draw half bottom ranges of std
            for i in range(int(N / 2), N):
                color = gen_color(c1_std, c2_std, mix= (i * 2 / N) - 1)
                up_std, down_std = sorted_acts_std[:, i], sorted_acts_std[:, i + 1]
                plt.fill_between(slices, up_std, down_std, facecolor=color, alpha=alpha)
                
            # Draw line of 50%
            plt.plot(sorted_acts_std[:, int(N / 2)], color=c1_std)

            # Draw half top ranges of comparison layer
            c1_comp = colors[label]
            c2_comp = lighten_color(c1_comp, amount=amount)
            for i in range(int(N / 2)):
                color = gen_color(c1_comp, c2_comp, mix= 1 - (i * 2 / N))
                up_comp, down_comp = sorted_acts[:, i], sorted_acts[:, i + 1]
                plt.fill_between(slices, up_comp, down_comp, facecolor=color, alpha=alpha)

            # Draw half bottom ranges of comparison layer
            for i in range(int(N / 2), N):
                color = gen_color(c1_comp, c2_comp, mix= (i * 2 / N) - 1)
                up_comp, down_comp = sorted_acts[:, i], sorted_acts[:, i + 1]
                plt.fill_between(slices, up_comp, down_comp, facecolor=color, alpha=alpha)
                    
            # Draw line of 50%
            plt.plot(sorted_acts[:, int(N / 2)], color=c1_comp)

            # labels
            std_patch = mpatches.Patch(color=c1_std, label=stdlabel)
            comp_patch = mpatches.Patch(color=c1_comp, label=label)
            plt.legend(handles=[std_patch, comp_patch])
            
            # titles
            plt.xticks(slices, sorted_slice, fontsize=3)
            plt.title(layer)
            plt.xlabel('slice')
            plt.ylabel('activations')
#             plt.savefig('{}/comp-with-label-q{}-{}-{}-{}.pdf'.format(dirpath, N, stdlabel, label, layer))
#             plt.clf()
            plt.show()

See conv with color gradients

# Read all activation values
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
import matplotlib.patches as mpatches
dirpath = '/Users/haekyu/data/interpretable-ml/result/act'
paradirpath = '/Users/haekyu/data/interpretable-ml/result/paras'

activation_all = pd.read_csv('./act_all.tsv', sep='\t')

# Parse data by labels
from collections import defaultdict
import numpy as np
from time import time

act_dict = defaultdict(lambda: defaultdict(lambda: {}))
total, tic = len(activation_all), time()
for index, row in activation_all.iterrows():
    if index % 3000 == 0:
        toc = time()
        print('    %d/%d=%.2lf, %.2lf sec' % (index, total, index / total, toc - tic))
        tic = time()
    label, layer, th, all_acts = row
    all_acts = np.array(all_acts.split(',')).astype(float)
    act_dict[label][layer][th] = all_acts

    0/12160=0.00, 0.00 sec
    3000/12160=0.25, 8.05 sec
    6000/12160=0.49, 7.49 sec
    9000/12160=0.74, 7.47 sec
    12000/12160=0.99, 7.87 sec

# Get percentile of the activations
from time import time

tic = time()

layernames = ['conv1', 'pool1', 'conv2', 'pool2', 'fc1']
act_Q_dict = defaultdict(lambda: defaultdict(lambda: []))
N = 20
for label in range(10):
    for layer in layernames:
        act_label_layer = act_dict[label][layer]
        for sliceth in act_label_layer:
            slice_act = act_label_layer[sliceth]
            Qs = [np.quantile(slice_act, q) for q in np.arange(0, 1.01, 1 / N)]
            act_Q_dict[label][layer].append(np.array(Qs))
        act_Q_dict[label][layer] = np.array(act_Q_dict[label][layer])

toc = time()
print('     Time: %.2lf sec' % (toc - tic))

     Time: 27.40 sec

import matplotlib.pyplot as plt
import numpy as np
from matplotlib.colors import rgb2hex

def gen_color(c1, c2, mix=0):
    '''
    Generate color which internally divides c1 and c2 with ratio of (1-mix: mix).
    The color is generated by linear interpolate from color c1 to c2.
    It returns c1 if mix=0, c2 if mix=1.
    * input
        - c1: first color in '#......' format
        - c2: second color in '#......' format
        - mix: internally dividing ratio between c1 and c2, in range [0, 1]. 
    * output
        - c: the output color, or an internally dividing points between c1 and c2
    '''

    # Check the validity of input
    assert len(c1) == len(c2)
    assert mix >= 0 and mix <= 1, 'mix=' + str(mix)
    
    # Extract RGB of c1 and c2
    rgb1 = np.array([int(c1[i : i + 2], 16) for i in range(1, len(c1), 2)])
    rgb2 = np.array([int(c2[i : i + 2], 16) for i in range(1, len(c2), 2)])
    
    # Get RGB of the output color c
    # by computing the internally dividing points between c1 and c2
    rgb = ((1 - mix) * rgb1 + mix * rgb2).astype(int)
    c = '#'+('{:}'*3).format(*[hex(a)[2:].zfill(2) for a in rgb])
    
    return c

import colorsys
import matplotlib.colors as mc

def lighten_color(color, amount=0.5):
    '''
    Lightens the given color by multiplying by the given amount.
    * input
        - color: input color in hex ('#......') format
        - amount: 1-luminosity. The lower, the more lightened.
    * output
        - c: lightened color in hex ('#......') format
    '''
    
    color = colorsys.rgb_to_hls(*mc.to_rgb(color))
    c = colorsys.hls_to_rgb(color[0], 1 - amount * (1 - color[1]), color[2])
    c = np.array(c) * 255
    c = c.astype(int)
    c_hex = '#'+('{:}'*3).format(*[hex(a)[2:].zfill(2) for a in c])

    return c_hex

import matplotlib.patches as mpatches
    
colors = ['#FF5733', '#FF3633', '#93FF33', '#3399FF', '#33FF80', '#7133FF', '#FFF033', '#FF33A8', '#FF3368', '#EF97F3']

alpha = 0.4
amount = 0.2
'''
Before Bias, compare a standard label and each other label
'''
for stdlabel in range(1):
    for layer in ['conv2']:
        
        # Sort the slice by standard label
        acts_label_std = act_Q_dict[stdlabel][layer]
        med_acts_label_std = acts_label_std[:, 1]
        sorted_slice = np.argsort(-np.array(med_acts_label_std)).astype(int)
        sorted_acts_std = np.array([acts_label_std[s] for s in sorted_slice])
        slices = list(range(len(sorted_slice)))
        
        for label in range(3, 4):
            if label == stdlabel:
                continue
        
            # Activation of comparison layer
            layer_acts = act_Q_dict[label][layer]
            sorted_acts = np.array([layer_acts[s] for s in sorted_slice])

            # Draw half top ranges of std
            c1_std = colors[stdlabel]
            c2_std = lighten_color(c1_std, amount=amount)
            for i in range(int(N / 2)):
                color = gen_color(c1_std, c2_std, mix= 1 - (i * 2 / N))
                up_std, down_std = sorted_acts_std[:, i], sorted_acts_std[:, i + 1]
                plt.fill_between(slices, up_std, down_std, facecolor=color, alpha=alpha)

            # Draw half bottom ranges of std
            for i in range(int(N / 2), N):
                color = gen_color(c1_std, c2_std, mix= (i * 2 / N) - 1)
                up_std, down_std = sorted_acts_std[:, i], sorted_acts_std[:, i + 1]
                plt.fill_between(slices, up_std, down_std, facecolor=color, alpha=alpha)
                
            # Draw line of 50%
            plt.plot(sorted_acts_std[:, int(N / 2)], color=c1_std)

            # Draw half top ranges of comparison layer
            c1_comp = colors[label]
            c2_comp = lighten_color(c1_comp, amount=amount)
            for i in range(int(N / 2)):
                color = gen_color(c1_comp, c2_comp, mix= 1 - (i * 2 / N))
                up_comp, down_comp = sorted_acts[:, i], sorted_acts[:, i + 1]
                plt.fill_between(slices, up_comp, down_comp, facecolor=color, alpha=alpha)

            # Draw half bottom ranges of comparison layer
            for i in range(int(N / 2), N):
                color = gen_color(c1_comp, c2_comp, mix= (i * 2 / N) - 1)
                up_comp, down_comp = sorted_acts[:, i], sorted_acts[:, i + 1]
                plt.fill_between(slices, up_comp, down_comp, facecolor=color, alpha=alpha)
                    
            # Draw line of 50%
            plt.plot(sorted_acts[:, int(N / 2)], color=c1_comp)

            # labels
            std_patch = mpatches.Patch(color=c1_std, label=stdlabel)
            comp_patch = mpatches.Patch(color=c1_comp, label=label)
            plt.legend(handles=[std_patch, comp_patch])
            
            # titles
            plt.xticks(slices, sorted_slice, fontsize=3)
            plt.title(layer)
            plt.xlabel('slice')
            plt.ylabel('activations')
#             plt.savefig('{}/comp-with-label-q{}-{}-{}-{}.pdf'.format(dirpath, N, stdlabel, label, layer))
#             plt.clf()
            plt.show()

See conv all

# Read all activation values
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
import matplotlib.patches as mpatches
dirpath = '/Users/haekyu/data/interpretable-ml/result/act'
paradirpath = '/Users/haekyu/data/interpretable-ml/result/paras'

activation_all = pd.read_csv('./act_all.tsv', sep='\t')

activation_all[:3]

	label	layer	slice	acts
0	0	conv1	0	0.26658163265306123,0.24872448979591838,0.2665...
1	0	conv1	1	0.3673469387755102,0.3392857142857143,0.376275...
2	0	conv1	2	0.1989795918367347,0.1556122448979592,0.198979...

# Parse data by labels
from collections import defaultdict
import numpy as np
from time import time

act_dict = defaultdict(lambda: defaultdict(lambda: {}))
total, tic = len(activation_all), time()
for index, row in activation_all.iterrows():
    if index % 3000 == 0:
        toc = time()
        print('    %d/%d=%.2lf, %.2lf sec' % (index, total, index / total, toc - tic))
        tic = time()
    label, layer, th, all_acts = row
    all_acts = np.array(all_acts.split(',')).astype(float)
    act_dict[label][layer][th] = all_acts

    0/12160=0.00, 0.00 sec
    3000/12160=0.25, 10.06 sec
    6000/12160=0.49, 9.26 sec
    9000/12160=0.74, 9.14 sec
    12000/12160=0.99, 9.20 sec

# Get Q1, Q2, Q3 of the activations
layernames = ['conv1', 'pool1', 'conv2', 'pool2', 'fc1']
act_Q_dict = defaultdict(lambda: defaultdict(lambda: []))
for label in range(10):
    for layer in layernames:
        act_label_layer = act_dict[label][layer]
        for sliceth in act_label_layer:
            slice_act = act_label_layer[sliceth]
            q1 = np.quantile(slice_act, 0.25)
            q2 = np.quantile(slice_act, 0.5)
            q3 = np.quantile(slice_act, 0.75)
            act_Q_dict[label][layer].append(np.array([q1, q2, q3]))
        act_Q_dict[label][layer] = np.array(act_Q_dict[label][layer])

# Get slice order of label 0
act_Q_dict[0]['conv1']

array([[0.22704082, 0.25      , 0.27295918],
       [0.31377551, 0.3494898 , 0.38137755],
       [0.15688776, 0.1747449 , 0.19005102],
       [0.30102041, 0.33418367, 0.36607143],
       [0.03571429, 0.04464286, 0.05229592],
       [0.20153061, 0.22831633, 0.25382653],
       [0.11479592, 0.13520408, 0.15306122],
       [0.00382653, 0.00510204, 0.00637755],
       [0.10969388, 0.12244898, 0.13265306],
       [0.28443878, 0.32142857, 0.35331633],
       [0.31122449, 0.34311224, 0.37244898],
       [0.00127551, 0.00382653, 0.00510204],
       [0.26530612, 0.29846939, 0.33035714],
       [0.24872449, 0.27295918, 0.29719388],
       [0.24362245, 0.27806122, 0.30994898],
       [0.0255102 , 0.03316327, 0.04081633],
       [0.26020408, 0.29336735, 0.32397959],
       [0.32908163, 0.36352041, 0.39540816],
       [0.26147959, 0.29336735, 0.32270408],
       [0.21428571, 0.25255102, 0.29209184],
       [0.06377551, 0.07780612, 0.08928571],
       [0.03188776, 0.03826531, 0.04719388],
       [0.18367347, 0.2130102 , 0.24107143],
       [0.02678571, 0.03443878, 0.04336735],
       [0.11479592, 0.12755102, 0.14030612],
       [0.08928571, 0.10714286, 0.12244898],
       [0.02295918, 0.03061224, 0.03826531],
       [0.10076531, 0.10969388, 0.11734694],
       [0.12755102, 0.14923469, 0.16836735],
       [0.31760204, 0.35459184, 0.38647959],
       [0.0625    , 0.07397959, 0.08418367],
       [0.19387755, 0.22831633, 0.25892857]])

colors = ['red', 'orange', 'green', 'blue', 'indianred', 'y', 'gold', 'indigo', 'hotpink', 'lightcoral']

'''
Before Bias, compare a standard label and each other label
'''
for stdlabel in range(10):
    for layer in ['conv2']:
        # Sort the slice by standard label
        acts_label_std = act_Q_dict[stdlabel][layer]
        med_acts_label_std = acts_label_std[:, 1]
        sorted_slice = np.argsort(-np.array(med_acts_label_std)).astype(int)
        sorted_acts_std = np.array([acts_label_std[s] for s in sorted_slice])
        slices = list(range(len(sorted_slice)))
        q1_std, q2_std, q3_std = sorted_acts_std[:, 0], sorted_acts_std[:, 1], sorted_acts_std[:, 2]

        for label in range(10):
            if label == stdlabel:
                continue
            layer_acts = act_Q_dict[label][layer]
            sorted_acts = np.array([layer_acts[s] for s in sorted_slice])

            q1_label, q2_label, q3_label = sorted_acts[:, 0], sorted_acts[:, 1], sorted_acts[:, 2]

            plt.fill_between(slices, q1_std, q3_std, facecolor=colors[stdlabel], alpha=0.6)
            plt.fill_between(slices, q1_label, q3_label, facecolor=colors[label], alpha=0.6)
            plt.xticks(slices, sorted_slice, fontsize=3)
            plt.legend((stdlabel , label))
            plt.title(layer)
            plt.xlabel('slice')
            plt.ylabel('activations')
            plt.savefig('{}/comp-with-label-q123-{}-{}-{}.pdf'.format(dirpath, stdlabel, label, layer))
            plt.clf()

<Figure size 432x288 with 0 Axes>

'''
Before Bias, compare 0 and all
'''
for layer in ['conv2']:
    # Sort the slice by label 0
    acts_label0 = act_Q_dict[0][layer]
    med_acts_label0 = acts_label0[:, 1]
    sorted_slice = np.argsort(-np.array(med_acts_label0)).astype(int)
    sorted_acts_0 = np.array([acts_label0[s] for s in sorted_slice])
    slices = list(range(len(sorted_slice)))
    q1_0, q2_0, q3_0 = sorted_acts_0[:, 0], sorted_acts_0[:, 1], sorted_acts_0[:, 2]
    plt.fill_between(slices, q1_0, q3_0, facecolor=colors[0], alpha=0.6)
    
    for label in range(1, 10):
        layer_acts = act_Q_dict[label][layer]
        sorted_acts = np.array([layer_acts[s] for s in sorted_slice])
        
        q1_label, q2_label, q3_label = sorted_acts[:, 0], sorted_acts[:, 1], sorted_acts[:, 2]
        plt.fill_between(slices, q1_label, q3_label, facecolor=colors[label], alpha=0.6)
        
    patches = [mpatches.Patch(color=c, label=th) for th, c in enumerate(colors[:10])]
    plt.legend(handles=patches)
    plt.xticks(slices, sorted_slice, fontsize=3)
    plt.title(layer)
    plt.xlabel('slice')
    plt.ylabel('activations')
    plt.savefig('{}/comp-with-label-q123-0-all-{}.pdf'.format(dirpath, layer))
    plt.clf()
    

<Figure size 432x288 with 0 Axes>

See conv median

# Read actiavtion values
import pandas as pd
activation_df = pd.read_csv('./act.csv')

from collections import defaultdict
activations = defaultdict(lambda: defaultdict(lambda: {}))
for index, row in activation_df.iterrows():
    label, layer, th, med = row
    activations[label][layer][th] = med

# Sort the slices
cell_sort = defaultdict(lambda: {})
layernames = ['conv1', 'pool1', 'conv2', 'pool2', 'fc1']
for label in range(10):
    for layer in layernames:
        slice_activation_map = activations[label][layer]
        sorted_slice = sorted(slice_activation_map, key=slice_activation_map.get)
        cell_sort[label][layer] = sorted_slice

# Print the cell_sort
for layer in layernames:
    print('      ------{}------'.format(layer))
    for label in range(10):
        sorted_slice = cell_sort[label][layer]
        print('      {}\t{}'.format(label, sorted_slice))

      ------conv1------
      0	[11, 7, 26, 15, 23, 21, 4, 30, 20, 25, 27, 8, 24, 6, 28, 2, 22, 5, 31, 0, 19, 13, 14, 16, 18, 12, 9, 3, 10, 1, 29, 17]
      1	[11, 7, 26, 23, 15, 20, 21, 30, 25, 4, 28, 6, 24, 8, 27, 2, 22, 5, 31, 19, 0, 14, 13, 18, 16, 9, 3, 12, 29, 1, 17, 10]
      2	[11, 7, 15, 4, 21, 26, 23, 30, 20, 27, 24, 8, 25, 6, 28, 2, 22, 31, 5, 0, 19, 13, 16, 18, 14, 12, 9, 10, 3, 1, 29, 17]
      3	[11, 7, 21, 4, 15, 26, 23, 30, 20, 27, 24, 8, 25, 6, 2, 28, 22, 31, 5, 0, 19, 13, 16, 18, 14, 12, 9, 10, 3, 1, 29, 17]
      4	[11, 7, 26, 15, 23, 21, 4, 30, 20, 25, 24, 8, 27, 6, 28, 2, 22, 31, 5, 19, 0, 14, 13, 16, 18, 12, 9, 3, 1, 10, 29, 17]
      5	[11, 7, 21, 4, 15, 26, 23, 27, 30, 20, 24, 8, 25, 6, 28, 2, 31, 22, 5, 0, 19, 13, 18, 14, 16, 12, 9, 10, 3, 1, 29, 17]
      6	[11, 7, 15, 26, 23, 21, 4, 30, 20, 25, 27, 24, 8, 28, 6, 2, 22, 31, 5, 19, 0, 13, 14, 18, 16, 12, 9, 3, 1, 10, 29, 17]
      7	[11, 7, 15, 26, 23, 21, 4, 30, 20, 25, 27, 24, 8, 28, 6, 2, 22, 31, 5, 19, 0, 14, 13, 18, 16, 12, 9, 3, 10, 29, 1, 17]
      8	[11, 7, 15, 23, 21, 26, 4, 30, 20, 25, 27, 8, 24, 6, 28, 2, 22, 31, 5, 19, 0, 14, 13, 18, 16, 12, 9, 3, 10, 1, 29, 17]
      9	[11, 7, 23, 15, 26, 21, 4, 20, 30, 25, 27, 24, 8, 6, 28, 2, 22, 31, 5, 19, 0, 14, 13, 16, 18, 12, 9, 3, 10, 1, 29, 17]
      ------pool1------
      0	[11, 7, 15, 23, 26, 21, 4, 20, 30, 25, 27, 8, 24, 6, 28, 2, 5, 31, 0, 22, 13, 14, 19, 16, 18, 12, 9, 3, 10, 1, 29, 17]
      1	[7, 11, 26, 23, 15, 21, 30, 20, 4, 25, 28, 6, 24, 8, 27, 2, 5, 22, 31, 0, 14, 19, 13, 18, 9, 3, 1, 12, 16, 29, 10, 17]
      2	[7, 11, 4, 15, 21, 26, 23, 27, 30, 20, 24, 25, 8, 6, 28, 2, 31, 5, 0, 22, 13, 19, 14, 16, 12, 18, 9, 10, 3, 1, 29, 17]
      3	[11, 7, 21, 4, 15, 26, 23, 27, 30, 20, 24, 8, 25, 6, 2, 28, 31, 5, 0, 22, 13, 19, 16, 12, 14, 18, 9, 10, 3, 1, 17, 29]
      4	[7, 11, 23, 26, 15, 21, 4, 30, 20, 25, 24, 27, 28, 8, 6, 2, 5, 31, 22, 0, 19, 13, 14, 16, 18, 12, 9, 3, 1, 29, 10, 17]
      5	[11, 7, 21, 4, 15, 26, 23, 27, 30, 20, 24, 8, 25, 6, 28, 2, 31, 5, 0, 22, 13, 19, 14, 16, 18, 12, 9, 10, 3, 1, 29, 17]
      6	[11, 7, 15, 26, 23, 21, 4, 30, 20, 25, 27, 24, 8, 28, 6, 2, 31, 5, 0, 22, 19, 13, 14, 18, 12, 16, 3, 9, 1, 10, 29, 17]
      7	[11, 7, 15, 23, 26, 21, 4, 30, 20, 27, 25, 24, 28, 8, 6, 2, 5, 31, 22, 0, 19, 13, 14, 18, 12, 16, 3, 9, 10, 29, 1, 17]
      8	[11, 7, 15, 21, 23, 4, 26, 30, 20, 27, 25, 8, 24, 6, 28, 2, 5, 31, 0, 22, 13, 14, 19, 16, 18, 12, 9, 3, 1, 10, 29, 17]
      9	[11, 7, 23, 15, 26, 21, 4, 30, 20, 25, 27, 24, 8, 6, 28, 2, 5, 22, 31, 0, 13, 14, 19, 16, 18, 12, 3, 9, 10, 1, 29, 17]
      ------conv2------
      0	[41, 24, 8, 32, 54, 30, 50, 2, 13, 55, 3, 63, 16, 34, 60, 9, 12, 14, 52, 37, 33, 47, 38, 35, 36, 39, 26, 61, 7, 1, 21, 11, 58, 25, 5, 18, 56, 31, 43, 46, 10, 48, 17, 4, 29, 57, 15, 27, 45, 22, 20, 53, 19, 51, 0, 28, 49, 62, 23, 42, 44, 59, 40, 6]
      1	[24, 41, 3, 50, 55, 21, 30, 54, 32, 46, 34, 13, 61, 8, 14, 60, 62, 2, 39, 63, 33, 9, 37, 47, 58, 25, 31, 43, 48, 16, 5, 26, 52, 7, 38, 12, 51, 56, 15, 35, 36, 53, 0, 11, 18, 19, 4, 17, 20, 57, 22, 1, 23, 27, 45, 10, 42, 44, 29, 59, 28, 40, 49, 6]
      2	[41, 32, 63, 2, 8, 54, 24, 13, 12, 3, 50, 52, 60, 47, 9, 30, 55, 34, 36, 37, 14, 16, 58, 26, 39, 5, 11, 31, 7, 33, 38, 18, 25, 48, 61, 1, 35, 56, 21, 43, 15, 10, 46, 4, 57, 17, 27, 19, 29, 51, 0, 53, 20, 45, 22, 23, 62, 28, 49, 42, 44, 59, 40, 6]
      3	[41, 63, 32, 2, 8, 13, 54, 30, 3, 12, 24, 60, 50, 52, 47, 9, 14, 16, 36, 34, 55, 37, 58, 7, 11, 33, 39, 26, 31, 5, 25, 38, 61, 18, 43, 21, 15, 46, 35, 10, 48, 56, 1, 4, 17, 57, 20, 0, 29, 45, 51, 19, 53, 27, 22, 62, 23, 28, 49, 42, 44, 59, 40, 6]
      4	[41, 24, 54, 32, 3, 50, 13, 55, 8, 30, 2, 63, 34, 14, 60, 9, 61, 37, 52, 31, 47, 12, 16, 38, 39, 58, 7, 21, 26, 46, 48, 33, 5, 25, 35, 11, 36, 43, 15, 56, 1, 62, 51, 17, 19, 53, 0, 57, 18, 4, 20, 22, 27, 45, 29, 10, 23, 28, 42, 44, 59, 49, 40, 6]
      5	[41, 32, 63, 8, 2, 54, 47, 50, 30, 3, 12, 13, 24, 60, 9, 52, 55, 34, 14, 36, 37, 58, 16, 11, 7, 31, 39, 33, 5, 26, 25, 48, 10, 61, 21, 18, 35, 38, 43, 46, 1, 15, 56, 0, 4, 17, 57, 22, 29, 51, 53, 45, 27, 19, 20, 62, 23, 28, 42, 49, 44, 59, 40, 6]
      6	[41, 24, 32, 54, 50, 2, 8, 63, 13, 30, 3, 9, 34, 52, 55, 14, 60, 12, 16, 37, 47, 39, 58, 61, 21, 26, 36, 31, 33, 35, 48, 11, 25, 46, 56, 38, 1, 5, 7, 43, 18, 15, 17, 51, 10, 57, 4, 53, 62, 0, 19, 27, 22, 29, 45, 20, 23, 28, 42, 44, 59, 49, 40, 6]
      7	[41, 32, 24, 30, 54, 50, 55, 63, 3, 60, 8, 2, 13, 52, 34, 14, 37, 47, 12, 9, 7, 16, 31, 39, 58, 61, 11, 36, 25, 33, 43, 5, 21, 26, 38, 46, 15, 35, 48, 51, 56, 18, 62, 4, 17, 19, 53, 20, 0, 1, 57, 22, 27, 10, 45, 23, 29, 28, 42, 44, 49, 59, 40, 6]
      8	[41, 24, 30, 8, 32, 54, 2, 63, 50, 13, 9, 14, 34, 55, 60, 3, 16, 52, 12, 33, 36, 37, 47, 61, 7, 39, 58, 11, 21, 38, 26, 35, 25, 31, 46, 18, 43, 48, 1, 5, 56, 10, 15, 57, 17, 4, 22, 45, 51, 29, 53, 0, 20, 19, 27, 62, 28, 23, 49, 42, 44, 59, 40, 6]
      9	[41, 24, 32, 30, 54, 50, 8, 63, 2, 3, 13, 34, 55, 14, 60, 9, 52, 16, 37, 61, 7, 12, 47, 33, 39, 58, 36, 21, 31, 43, 46, 26, 11, 25, 38, 35, 48, 5, 15, 56, 18, 1, 17, 51, 53, 62, 4, 19, 20, 57, 22, 0, 10, 45, 27, 29, 23, 28, 42, 44, 59, 49, 40, 6]
      ------pool2------
      0	[41, 24, 30, 8, 32, 54, 2, 16, 50, 3, 55, 13, 34, 63, 9, 14, 33, 12, 37, 52, 60, 7, 25, 26, 35, 38, 47, 1, 21, 31, 39, 61, 5, 11, 18, 36, 56, 10, 17, 29, 48, 43, 45, 57, 15, 58, 4, 46, 22, 53, 20, 23, 27, 28, 49, 0, 19, 51, 42, 62, 44, 59, 40, 6]
      1	[24, 41, 3, 21, 30, 50, 32, 34, 54, 55, 46, 61, 8, 13, 14, 33, 39, 60, 2, 16, 62, 63, 5, 9, 25, 31, 37, 47, 48, 52, 7, 26, 43, 15, 53, 56, 58, 12, 20, 35, 36, 51, 57, 17, 18, 38, 0, 4, 11, 19, 22, 23, 45, 1, 10, 27, 42, 29, 44, 59, 28, 40, 49, 6]
      2	[41, 2, 32, 54, 63, 8, 12, 24, 30, 52, 3, 13, 50, 9, 16, 34, 37, 47, 14, 55, 60, 26, 31, 33, 5, 25, 36, 7, 39, 48, 58, 11, 18, 38, 1, 10, 35, 56, 61, 15, 17, 21, 43, 45, 4, 57, 29, 46, 53, 19, 20, 23, 0, 27, 51, 22, 28, 42, 49, 62, 44, 59, 40, 6]
      3	[41, 30, 32, 54, 63, 2, 12, 13, 24, 52, 3, 8, 16, 47, 50, 9, 14, 33, 37, 7, 31, 34, 36, 60, 25, 55, 5, 11, 26, 39, 38, 58, 61, 15, 18, 43, 48, 10, 21, 17, 35, 45, 56, 57, 1, 4, 20, 29, 46, 53, 51, 0, 19, 22, 23, 28, 27, 62, 42, 49, 44, 59, 40, 6]
      4	[41, 24, 54, 32, 3, 30, 50, 55, 2, 8, 13, 34, 63, 31, 37, 9, 14, 16, 52, 25, 26, 33, 47, 48, 60, 61, 5, 7, 12, 39, 21, 35, 38, 56, 11, 15, 17, 53, 58, 1, 18, 36, 43, 46, 51, 57, 62, 4, 19, 20, 45, 0, 22, 23, 29, 10, 27, 42, 44, 28, 59, 49, 40, 6]
      5	[41, 63, 32, 2, 8, 30, 47, 54, 3, 12, 24, 9, 50, 52, 13, 14, 16, 34, 55, 60, 37, 7, 31, 33, 36, 5, 11, 25, 26, 48, 58, 10, 39, 18, 21, 15, 35, 38, 45, 56, 57, 61, 1, 4, 17, 29, 43, 53, 0, 46, 51, 19, 20, 22, 23, 28, 27, 62, 42, 44, 49, 59, 40, 6]
      6	[41, 24, 30, 2, 32, 50, 54, 3, 8, 34, 63, 9, 52, 55, 13, 14, 16, 37, 12, 33, 25, 31, 39, 47, 48, 60, 21, 26, 61, 35, 36, 1, 5, 7, 11, 56, 58, 10, 17, 46, 15, 18, 38, 43, 45, 51, 57, 23, 53, 4, 19, 29, 22, 62, 0, 20, 27, 42, 28, 44, 59, 49, 40, 6]
      7	[41, 30, 32, 54, 24, 50, 55, 8, 37, 63, 2, 3, 34, 47, 52, 60, 12, 14, 31, 7, 13, 9, 16, 25, 39, 5, 11, 15, 21, 26, 33, 43, 48, 35, 36, 46, 61, 38, 56, 17, 18, 53, 58, 4, 19, 45, 51, 57, 62, 0, 10, 20, 22, 1, 23, 29, 27, 44, 28, 42, 40, 49, 59, 6]
      8	[41, 24, 30, 32, 2, 8, 54, 9, 16, 63, 34, 50, 52, 3, 12, 13, 14, 33, 55, 60, 7, 31, 37, 47, 5, 21, 25, 26, 36, 39, 61, 35, 38, 48, 1, 11, 15, 18, 45, 58, 10, 43, 56, 57, 29, 53, 4, 17, 46, 51, 20, 22, 0, 19, 23, 28, 27, 42, 62, 44, 49, 59, 40, 6]
      9	[41, 24, 30, 32, 2, 50, 54, 3, 8, 34, 55, 63, 16, 37, 52, 7, 13, 14, 31, 9, 12, 39, 47, 60, 26, 33, 5, 21, 25, 36, 48, 61, 15, 35, 46, 11, 18, 43, 56, 58, 38, 53, 1, 4, 10, 17, 51, 57, 19, 20, 22, 29, 45, 62, 0, 23, 42, 27, 28, 44, 59, 40, 49, 6]
      ------fc1------
      0	[3, 4, 6, 7, 8, 9, 10, 12, 15, 16, 19, 20, 21, 22, 24, 25, 29, 30, 32, 33, 35, 36, 39, 41, 47, 48, 51, 52, 64, 75, 76, 77, 78, 80, 81, 82, 87, 91, 94, 95, 97, 98, 102, 103, 104, 105, 108, 109, 110, 111, 113, 115, 116, 117, 118, 119, 124, 129, 131, 133, 134, 136, 138, 141, 142, 145, 148, 151, 157, 158, 161, 166, 168, 169, 170, 171, 173, 177, 179, 182, 193, 198, 199, 205, 206, 207, 208, 209, 210, 212, 215, 216, 218, 221, 223, 224, 225, 227, 229, 235, 236, 240, 243, 246, 247, 256, 259, 260, 262, 265, 267, 271, 272, 273, 274, 277, 278, 280, 286, 288, 295, 297, 298, 299, 300, 302, 308, 311, 312, 313, 315, 316, 318, 320, 323, 324, 325, 330, 334, 335, 338, 341, 342, 346, 349, 350, 351, 353, 355, 356, 357, 359, 360, 361, 362, 364, 366, 368, 371, 376, 379, 382, 383, 385, 390, 391, 393, 394, 396, 398, 400, 401, 402, 406, 407, 411, 412, 413, 415, 419, 420, 421, 423, 424, 427, 429, 430, 432, 433, 434, 439, 440, 446, 448, 449, 451, 453, 462, 463, 464, 465, 466, 467, 469, 473, 474, 478, 479, 481, 487, 489, 491, 492, 494, 497, 500, 501, 509, 512, 513, 514, 515, 519, 523, 525, 529, 530, 533, 539, 543, 546, 548, 550, 552, 554, 556, 557, 560, 570, 571, 572, 573, 574, 581, 587, 588, 589, 594, 595, 596, 597, 602, 603, 606, 607, 608, 609, 610, 612, 614, 616, 617, 623, 625, 626, 634, 635, 638, 640, 641, 642, 643, 644, 645, 646, 647, 648, 651, 652, 653, 660, 663, 665, 667, 668, 671, 672, 684, 685, 686, 687, 689, 691, 694, 697, 698, 700, 701, 702, 706, 710, 716, 718, 726, 728, 734, 739, 741, 744, 748, 757, 758, 760, 762, 763, 766, 768, 770, 771, 772, 773, 774, 776, 777, 778, 779, 782, 783, 784, 786, 787, 788, 790, 792, 794, 797, 801, 802, 806, 809, 810, 811, 815, 817, 822, 825, 828, 831, 834, 837, 845, 846, 851, 852, 854, 855, 856, 857, 858, 863, 865, 866, 868, 869, 872, 873, 875, 885, 886, 889, 890, 891, 894, 895, 896, 898, 901, 906, 908, 909, 910, 911, 916, 917, 921, 923, 931, 937, 938, 941, 942, 945, 948, 953, 954, 955, 956, 957, 959, 961, 962, 963, 965, 966, 967, 969, 975, 976, 980, 983, 987, 988, 990, 991, 997, 1000, 1001, 1010, 1011, 1016, 1017, 1022, 1023, 428, 628, 164, 418, 84, 627, 483, 804, 354, 144, 995, 881, 305, 798, 992, 83, 848, 197, 254, 268, 234, 67, 719, 1015, 842, 924, 631, 477, 979, 761, 775, 74, 682, 747, 830, 112, 220, 27, 196, 46, 58, 62, 713, 496, 555, 340, 673, 821, 880, 813, 38, 732, 201, 61, 387, 42, 871, 1008, 461, 327, 849, 751, 964, 336, 296, 128, 442, 583, 437, 120, 807, 377, 486, 915, 781, 156, 257, 558, 339, 149, 540, 887, 372, 704, 862, 752, 250, 819, 632, 522, 982, 578, 940, 188, 932, 563, 153, 289, 101, 255, 520, 405, 690, 438, 455, 622, 591, 874, 639, 791, 127, 844, 1019, 459, 936, 230, 1013, 322, 287, 65, 1002, 538, 971, 266, 443, 729, 985, 745, 902, 925, 755, 521, 859, 727, 789, 576, 535, 970, 877, 724, 703, 754, 688, 947, 913, 753, 680, 292, 517, 764, 241, 414, 348, 708, 808, 125, 650, 43, 814, 840, 707, 472, 426, 812, 56, 893, 968, 884, 445, 864, 130, 577, 231, 805, 11, 493, 624, 71, 615, 291, 90, 410, 450, 567, 50, 416, 998, 637, 579, 978, 1004, 712, 599, 832, 531, 242, 926, 332, 613, 853, 524, 475, 363, 226, 656, 841, 717, 244, 785, 912, 918, 721, 670, 389, 564, 251, 249, 562, 373, 824, 630, 1012, 981, 139, 347, 417, 547, 620, 759, 999, 147, 49, 502, 374, 511, 816, 861, 397, 13, 725, 163, 586, 301, 253, 949, 506, 328, 1005, 730, 114, 232, 319, 658, 1021, 860, 457, 604, 678, 321, 233, 121, 544, 566, 507, 899, 662, 867, 304, 352, 495, 282, 395, 883, 310, 508, 195, 239, 471, 749, 264, 72, 818, 743, 952, 575, 534, 190, 0, 456, 403, 2, 194, 63, 592, 568, 633, 740, 714, 549, 870, 454, 935, 392, 252, 309, 973, 920, 731, 1018, 922, 532, 796, 518, 836, 45, 485, 431, 441, 657, 276, 337, 245, 765, 933, 709, 306, 155, 705, 820, 422, 674, 159, 847, 839, 903, 26, 598, 767, 735, 553, 135, 498, 137, 585, 66, 677, 34, 248, 345, 984, 333, 1020, 237, 57, 850, 476, 458, 54, 892, 146, 263, 580, 408, 943, 679, 561, 958, 536, 907, 228, 829, 528, 214, 213, 795, 68, 827, 480, 303, 934, 951, 833, 527, 1003, 711, 722, 551, 447, 59, 367, 140, 723, 545, 211, 258, 143, 593, 85, 919, 191, 742, 960, 974, 835, 275, 600, 715, 720, 281, 621, 444, 189, 92, 541, 217, 162, 370, 756, 269, 404, 977, 358, 484, 378, 18, 93, 605, 692, 699, 380, 946, 152, 381, 73, 681, 996, 178, 590, 490, 939, 219, 972, 69, 17, 160, 222, 202, 187, 876, 238, 1014, 516, 510, 107, 70, 452, 800, 666, 53, 184, 122, 611, 695, 150, 675, 468, 664, 799, 737, 329, 505, 882, 96, 618, 504, 23, 399, 914, 343, 676, 470, 944, 326, 900, 738, 317, 823, 123, 331, 649, 843, 542, 409, 100, 261, 425, 838, 780, 192, 175, 582, 986, 746, 482, 375, 180, 803, 369, 736, 167, 126, 994, 460, 365, 569, 314, 584, 826, 659, 769, 186, 14, 290, 5, 636, 388, 929, 750, 927, 930, 279, 683, 878, 294, 40, 928, 154, 284, 793, 28, 203, 897, 435, 200, 132, 733, 174, 661, 285, 879, 79, 499, 654, 165, 669, 488, 619, 526, 1009, 88, 565, 993, 60, 696, 176, 283, 601, 31, 344, 629, 44, 1, 989, 106, 270, 185, 99, 1006, 89, 693, 950, 904, 293, 307, 1007, 183, 55, 436, 386, 537, 204, 86, 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519, 521, 529, 530, 533, 540, 543, 546, 548, 550, 552, 554, 555, 556, 557, 560, 563, 570, 572, 573, 574, 576, 577, 579, 581, 587, 588, 589, 591, 594, 595, 596, 597, 602, 606, 607, 608, 609, 610, 612, 614, 615, 616, 623, 626, 632, 634, 638, 640, 641, 642, 643, 644, 646, 647, 651, 652, 660, 662, 663, 664, 665, 668, 671, 672, 676, 682, 684, 685, 687, 689, 691, 697, 698, 700, 701, 702, 706, 708, 710, 713, 716, 718, 724, 726, 732, 734, 739, 741, 744, 748, 755, 757, 758, 763, 764, 765, 770, 771, 772, 773, 774, 776, 777, 778, 779, 781, 783, 784, 786, 787, 794, 797, 799, 801, 802, 804, 806, 807, 809, 810, 815, 820, 822, 825, 828, 830, 831, 832, 833, 834, 835, 841, 845, 846, 851, 854, 855, 856, 859, 860, 863, 865, 868, 869, 872, 873, 875, 885, 886, 889, 890, 891, 894, 896, 898, 906, 907, 908, 910, 911, 916, 917, 921, 923, 926, 931, 935, 937, 938, 942, 945, 948, 953, 956, 957, 959, 961, 962, 964, 965, 967, 969, 972, 975, 976, 980, 983, 984, 987, 988, 990, 991, 1001, 1010, 1015, 1017, 1020, 1022, 1023, 459, 901, 627, 395, 463, 657, 951, 190, 5, 791, 450, 483, 915, 752, 541, 189, 603, 508, 686, 130, 375, 202, 977, 411, 635, 527, 179, 462, 1011, 1012, 249, 92, 344, 407, 523, 645, 958, 219, 273, 798, 941, 201, 1006, 524, 351, 669, 195, 877, 304, 547, 135, 341, 132, 624, 26, 56, 963, 369, 89, 537, 769, 54, 617, 378, 549, 756, 749, 905, 667, 220, 761, 441, 83, 200, 230, 435, 430, 971, 1, 128, 125, 2, 897, 185, 637, 993, 966, 193, 276, 1018, 826, 674, 242, 228, 704, 186, 460, 598, 23, 290, 1019, 730, 653, 819, 736, 918, 3, 387, 788, 385, 99, 559, 107, 180, 422, 709, 551, 790, 148, 126, 1016, 418, 40, 233, 340, 544, 767, 164, 569, 900, 142, 939, 654, 679, 1008, 495, 60, 955, 796, 355, 339, 416, 824, 84, 858, 601, 57, 454, 613, 600, 585, 571, 414, 599, 611, 53, 534, 565, 329, 780, 321, 628, 934, 690, 336, 278, 237, 94, 992, 314, 678, 384, 531, 506, 711, 880, 510, 347, 995, 68, 631, 740, 766, 264, 93, 270, 656, 333, 234, 153, 426, 648, 146, 1007, 789, 158, 800, 187, 633, 929, 272, 950, 553, 296, 751, 954, 212, 821, 199, 996, 762, 494, 542, 268, 699, 469, 731, 443, 754, 505, 895, 444, 327, 452, 377, 373, 817, 795, 70, 331, 985, 680, 159, 269, 66, 857, 884, 904, 44, 670, 737, 539, 604, 292, 639, 455, 1013, 65, 792, 1004, 876, 241, 297, 47, 852, 728, 712, 518, 584, 567, 973, 532, 8, 738, 367, 811, 525, 279, 580, 930, 920, 793, 445, 213, 1003, 578, 522, 343, 464, 356, 255, 823, 409, 62, 434, 562, 326, 681, 745, 722, 309, 949, 586, 27, 439, 582, 69, 696, 814, 922, 451, 848, 97, 289, 620, 816, 725, 155, 175, 879, 284, 71, 844, 14, 106, 477, 836, 839, 803, 989, 447, 902, 120, 843, 310, 903, 502, 818, 719, 813, 217, 244, 397, 205, 503, 636, 127, 695, 893, 168, 150, 838, 11, 561, 618, 301, 259, 545, 621, 191, 829, 104, 116, 167, 940, 285, 986, 705, 488, 862, 694, 172, 847, 497, 496, 837, 528, 404, 163, 359, 486, 775, 735, 535, 928, 768, 707, 468, 866, 661, 203, 143, 659, 867, 605, 380, 936, 842, 927, 974, 431, 291, 38, 471, 746, 79, 160, 149, 258, 358, 147, 204, 328, 979, 252, 759, 275, 281, 485, 1009, 46, 438, 381, 625, 874, 399, 970, 507, 619, 750, 723, 630, 122, 360, 352, 881, 892, 883, 693, 753, 564, 861, 484, 154, 63, 511, 18, 688, 123, 675, 658, 742, 999, 853, 100, 812, 248, 919, 305, 760, 410, 482, 427, 253, 998, 717, 785, 583, 43, 978, 192, 968, 470, 282, 456, 1000, 245, 461, 374, 139, 59, 388, 137, 747, 55, 403, 145, 222, 196, 808, 513, 37, 538, 184, 480, 850, 254, 504, 42, 261, 239, 925, 677, 887, 650, 178, 238, 405, 520, 714, 472, 457, 827, 365, 1021, 121, 566, 782, 188, 303, 888, 517, 1002, 475, 1014, 924, 840, 864, 673, 666, 498, 437, 165, 370, 960, 947, 348, 692, 982, 729, 805, 251, 214, 490, 593, 211, 86, 101, 85, 231, 257, 592, 17, 526, 181, 743, 183, 727, 944, 88, 683, 194, 1005, 871, 994, 882, 849, 629, 932, 912, 232, 649, 307, 317, 176, 424, 61, 129, 458, 156, 28, 622, 590, 250, 899, 152, 306, 536, 568, 174, 428, 721, 655, 266, 283, 909, 933, 946, 35, 715, 720, 913, 914, 363, 319, 997, 493, 943, 322, 386, 140, 981, 703, 90, 558, 436, 575, 425, 96, 952, 878, 733, 226, 0, 31, 870]
      8	[4, 6, 7, 9, 10, 12, 13, 15, 16, 19, 20, 21, 22, 24, 25, 29, 32, 33, 36, 39, 41, 50, 51, 52, 53, 58, 64, 76, 77, 78, 80, 81, 82, 87, 91, 94, 95, 97, 98, 102, 103, 105, 108, 109, 110, 111, 113, 115, 117, 118, 119, 124, 125, 131, 133, 134, 136, 138, 141, 151, 157, 158, 159, 161, 166, 168, 169, 170, 171, 173, 177, 182, 193, 197, 198, 199, 206, 207, 208, 209, 210, 212, 215, 216, 218, 220, 221, 223, 224, 225, 227, 229, 233, 235, 236, 240, 243, 246, 247, 256, 260, 262, 265, 267, 271, 272, 274, 277, 280, 286, 288, 294, 297, 298, 299, 300, 302, 308, 310, 311, 312, 313, 315, 316, 318, 320, 323, 324, 325, 330, 334, 335, 337, 338, 340, 341, 342, 346, 349, 350, 353, 355, 357, 360, 361, 362, 364, 366, 368, 372, 376, 379, 382, 383, 390, 391, 393, 394, 396, 398, 400, 401, 402, 406, 412, 413, 415, 419, 420, 421, 423, 424, 429, 430, 433, 440, 446, 448, 449, 453, 462, 465, 466, 467, 473, 474, 478, 479, 481, 487, 489, 491, 492, 494, 495, 497, 500, 501, 509, 512, 514, 515, 519, 523, 525, 529, 530, 533, 539, 543, 546, 547, 548, 550, 552, 554, 555, 556, 557, 560, 570, 571, 572, 573, 574, 576, 577, 581, 583, 587, 588, 589, 591, 594, 595, 596, 597, 598, 602, 603, 606, 607, 608, 609, 610, 612, 614, 615, 616, 617, 623, 624, 626, 627, 632, 634, 638, 640, 641, 642, 643, 644, 645, 646, 647, 651, 652, 657, 660, 663, 665, 667, 668, 671, 672, 684, 685, 686, 687, 689, 691, 694, 697, 698, 700, 701, 702, 706, 710, 716, 718, 719, 726, 734, 739, 740, 741, 748, 754, 755, 757, 758, 760, 762, 763, 765, 767, 768, 770, 771, 772, 773, 774, 776, 777, 778, 779, 783, 784, 786, 787, 790, 791, 792, 794, 797, 801, 802, 804, 806, 809, 810, 815, 822, 825, 828, 833, 834, 844, 845, 846, 851, 852, 854, 855, 856, 857, 859, 863, 865, 866, 868, 869, 872, 873, 875, 877, 885, 886, 889, 890, 894, 896, 898, 901, 906, 908, 910, 911, 916, 917, 921, 923, 937, 938, 942, 945, 948, 953, 954, 956, 957, 959, 961, 962, 964, 965, 967, 969, 975, 976, 980, 982, 983, 987, 988, 990, 991, 1000, 1001, 1017, 1022, 1023, 761, 47, 339, 463, 517, 1015, 112, 738, 688, 635, 179, 128, 496, 985, 359, 830, 876, 439, 75, 690, 143, 408, 540, 881, 732, 713, 782, 837, 579, 144, 483, 148, 563, 367, 711, 327, 187, 142, 295, 309, 918, 764, 521, 427, 404, 354, 864, 811, 476, 336, 222, 418, 832, 18, 941, 205, 625, 442, 392, 147, 461, 49, 289, 842, 899, 292, 130, 259, 656, 57, 775, 544, 464, 681, 121, 190, 858, 417, 74, 781, 812, 1012, 189, 860, 1010, 371, 670, 999, 971, 451, 441, 248, 592, 735, 373, 997, 196, 1008, 972, 67, 569, 432, 926, 242, 637, 931, 30, 749, 712, 744, 1016, 653, 282, 352, 504, 1018, 273, 692, 553, 139, 1020, 850, 1019, 395, 254, 796, 499, 628, 831, 807, 375, 454, 201, 722, 167, 984, 745, 31, 333, 511, 1013, 766, 819, 69, 79, 639, 63, 296, 666, 679, 593, 973, 816, 567, 155, 883, 847, 304, 116, 438, 682, 506, 287, 840, 363, 261, 622, 2, 527, 817, 925, 450, 853, 83, 513, 736, 1005, 107, 380, 403, 11, 486, 490, 407, 664, 314, 38, 1009, 788, 730, 291, 951, 826, 154, 106, 410, 230, 836, 195, 437, 955, 356, 101, 66, 185, 966, 902, 507, 648, 1011, 8, 884, 798, 104, 974, 524, 153, 562, 578, 541, 949, 747, 92, 331, 252, 455, 528, 293, 54, 963, 880, 704, 994, 900, 940, 907, 162, 46, 498, 752, 922, 708, 40, 600, 202, 200, 411, 477, 986, 156, 471, 149, 3, 820, 813, 720, 992, 43, 795, 551, 175, 434, 34, 731, 35, 564, 278, 968, 725, 114, 249, 146, 895, 56, 695, 263, 493, 93, 1014, 909, 27, 1002, 605, 68, 659, 388, 502, 936, 707, 793, 705, 135, 253, 488, 981, 518, 905, 414, 727, 319, 351, 445, 662, 72, 585, 275, 1004, 673, 522, 892, 28, 160, 1021, 516, 485, 129, 756, 120, 73, 443, 658, 328, 601, 915, 584, 680, 870, 932, 48, 231, 751, 472, 874, 484, 301, 821, 387, 805, 389, 674, 934, 23, 933, 939, 322, 649, 257, 789, 912, 132, 843, 620, 824, 505, 65, 545, 1003, 137, 920, 904, 654, 283, 977, 510, 269, 482, 508, 469, 650, 621, 808, 1006, 862, 919, 84, 14, 542, 204, 255, 234, 979, 549, 237, 611, 737, 935, 559, 630, 123, 100, 60, 211, 839, 303, 150, 460, 447, 929, 728, 913, 835, 232, 631, 717, 71, 244, 803, 814, 799, 604, 276, 268, 946, 613, 88, 452, 575, 241, 618, 709, 878, 468, 228, 848, 180, 750, 633, 998, 568, 891, 879, 995, 61, 676, 841, 164, 924, 428, 305, 699, 345, 914, 536, 184, 531, 538, 459, 191, 425, 669, 213, 823, 62, 861, 281, 724, 145, 385, 186, 44, 59, 456, 279, 928, 566, 818, 871, 290, 535, 251, 89, 214, 537, 381, 729, 586, 90, 55, 444, 409, 480, 122, 458, 96, 405, 838, 377, 952, 661, 1, 238, 435, 927, 947, 165, 829, 590, 958, 370, 239, 317, 534, 26, 127, 457, 675, 203, 188, 178, 558, 42, 1007, 344, 743, 503, 386, 245, 397, 742, 785, 126, 565, 99, 655, 416, 5, 378, 849, 163, 217, 365, 45, 348, 0, 85, 580, 321, 996, 882, 140, 599, 721, 258, 629, 219, 723, 70, 780, 306, 683, 532, 358, 903, 86, 897, 329, 332, 769, 174, 526, 703, 753, 369, 943, 470, 194, 520, 250, 944, 978, 264, 887, 475, 693, 993, 867, 746, 374, 893, 347, 696, 426, 678, 715, 307, 800, 176, 677, 561, 436, 759, 636, 399, 431, 989, 172, 270, 181, 930, 970, 183, 326, 284, 733, 285, 384, 266, 152, 950, 226, 714, 582, 343, 192, 827, 960, 422, 888, 619, 37, 17]
      9	[4, 6, 7, 9, 10, 12, 13, 15, 16, 19, 20, 21, 22, 24, 25, 29, 30, 32, 33, 36, 39, 41, 47, 50, 51, 52, 58, 64, 75, 76, 77, 80, 81, 82, 87, 91, 95, 98, 102, 103, 105, 108, 109, 110, 111, 112, 113, 115, 117, 118, 119, 124, 131, 133, 134, 136, 138, 141, 144, 151, 157, 159, 161, 166, 169, 170, 171, 173, 177, 179, 182, 197, 198, 206, 207, 208, 209, 210, 215, 216, 221, 223, 224, 225, 227, 229, 235, 236, 240, 243, 246, 247, 255, 256, 260, 262, 265, 267, 271, 274, 277, 280, 286, 288, 292, 293, 294, 295, 298, 299, 300, 302, 308, 310, 311, 312, 313, 314, 315, 316, 318, 320, 323, 324, 325, 330, 334, 335, 336, 337, 338, 340, 342, 346, 349, 350, 353, 355, 357, 361, 362, 364, 366, 368, 371, 375, 376, 379, 382, 383, 390, 391, 392, 393, 394, 395, 396, 398, 400, 401, 402, 406, 408, 411, 412, 413, 415, 417, 419, 420, 421, 423, 429, 432, 440, 441, 442, 446, 448, 449, 453, 462, 465, 466, 467, 473, 474, 476, 478, 479, 481, 487, 489, 491, 492, 495, 499, 500, 501, 509, 514, 515, 519, 529, 530, 533, 543, 546, 547, 548, 549, 550, 552, 554, 555, 556, 557, 560, 563, 570, 571, 573, 574, 576, 577, 581, 587, 588, 589, 591, 594, 595, 596, 597, 602, 603, 606, 607, 608, 609, 610, 612, 614, 615, 616, 621, 623, 626, 627, 632, 634, 637, 638, 640, 641, 642, 643, 644, 645, 646, 647, 648, 651, 652, 656, 657, 660, 662, 663, 665, 668, 671, 672, 682, 684, 685, 687, 689, 690, 691, 697, 698, 700, 701, 702, 706, 710, 711, 713, 716, 718, 726, 731, 732, 734, 739, 741, 744, 748, 749, 752, 754, 755, 758, 763, 764, 767, 768, 770, 771, 772, 773, 774, 776, 777, 778, 779, 781, 783, 784, 786, 787, 791, 792, 793, 794, 797, 801, 802, 806, 809, 810, 815, 822, 825, 826, 828, 833, 834, 835, 845, 846, 851, 854, 855, 856, 859, 863, 865, 868, 869, 872, 873, 875, 877, 885, 886, 889, 890, 894, 896, 898, 906, 908, 910, 911, 916, 917, 921, 937, 938, 941, 942, 945, 948, 949, 953, 954, 956, 957, 961, 962, 965, 967, 969, 972, 975, 976, 980, 983, 984, 987, 988, 990, 1001, 1011, 1017, 1019, 1020, 1022, 1023, 598, 34, 804, 799, 452, 963, 923, 725, 974, 860, 49, 505, 669, 527, 534, 242, 789, 544, 559, 971, 107, 218, 751, 27, 367, 238, 918, 635, 798, 756, 985, 820, 964, 624, 418, 510, 341, 765, 94, 604, 664, 719, 653, 959, 735, 876, 1015, 842, 578, 659, 704, 72, 919, 128, 757, 69, 796, 143, 180, 738, 237, 187, 450, 11, 230, 1010, 572, 585, 329, 162, 253, 130, 301, 404, 901, 831, 905, 601, 234, 241, 276, 56, 524, 54, 373, 617, 332, 351, 907, 359, 435, 433, 220, 372, 190, 92, 287, 18, 146, 958, 935, 114, 200, 523, 521, 525, 531, 537, 1016, 463, 272, 818, 333, 248, 512, 539, 380, 736, 730, 352, 688, 8, 304, 1012, 212, 121, 68, 193, 579, 593, 195, 439, 67, 494, 766, 567, 628, 163, 389, 992, 263, 410, 807, 83, 694, 999, 686, 296, 542, 508, 1014, 900, 629, 71, 676, 931, 269, 430, 116, 290, 724, 154, 728, 309, 497, 106, 139, 254, 516, 137, 709, 951, 40, 864, 5, 569, 44, 540, 661, 407, 844, 926, 289, 93, 740, 233, 522, 455, 99, 459, 639, 431, 89, 745, 553, 636, 443, 282, 483, 454, 291, 360, 132, 331, 611, 480, 816, 891, 673, 211, 775, 14, 991, 658, 551, 26, 761, 53, 23, 681, 378, 788, 925, 920, 486, 57, 219, 297, 667, 817, 580, 811, 995, 1008, 284, 434, 403, 541, 852, 249, 339, 883, 989, 48, 625, 857, 354, 915, 1006, 270, 186, 202, 843, 85, 472, 1, 488, 147, 377, 832, 252, 850, 79, 742, 884, 717, 65, 447, 769, 84, 840, 879, 278, 175, 185, 861, 135, 445, 153, 279, 2, 399, 904, 584, 1004, 145, 468, 158, 387, 66, 464, 73, 1000, 565, 168, 795, 191, 184, 874, 737, 620, 384, 503, 485, 150, 535, 1021, 830, 862, 966, 502, 369, 924, 866, 46, 460, 125, 381, 838, 74, 666, 120, 1013, 345, 532, 97, 936, 327, 997, 3, 654, 204, 142, 222, 808, 561, 45, 358, 470, 538, 583, 78, 461, 148, 416, 63, 712, 100, 613, 881, 708, 927, 837, 867, 444, 929, 932, 762, 60, 189, 848, 934, 973, 385, 922, 451, 518, 517, 160, 126, 939, 477, 239, 670, 59, 993, 213, 586, 824, 902, 506, 129, 386, 261, 803, 696, 707, 858, 273, 1018, 482, 1003, 805, 199, 747, 750, 600, 693, 977, 800, 164, 31, 722, 201, 928, 819, 695, 104, 841, 244, 217, 438, 326, 496, 618, 363, 982, 564, 699, 425, 196, 633, 155, 940, 409, 790, 343, 35, 895, 897, 504, 307, 123, 490, 328, 1002, 319, 183, 178, 62, 723, 526, 156, 823, 853, 839, 887, 780, 829, 507, 590, 892, 61, 785, 782, 456, 405, 836, 493, 55, 847, 880, 1005, 631, 259, 605, 275, 511, 427, 592, 264, 321, 317, 251, 678, 674, 397, 692, 388, 176, 1007, 655, 903, 912, 88, 994, 1009, 268, 599, 705, 228, 680, 356, 428, 545, 414, 437, 622, 813, 231, 568, 729, 322, 944, 127, 232, 424, 562, 203, 43, 303, 426, 42, 86, 283, 955, 679, 821, 484, 469, 812, 513, 528, 727, 986, 305, 677, 996, 181, 968, 720, 471, 281, 933, 245, 899, 205, 38, 649, 675, 498, 760, 893, 970, 575, 365, 566, 172, 979, 882, 149, 258, 827, 814, 347, 947, 683, 167, 140, 37, 344, 952, 650, 422, 746, 871, 950, 759, 909, 930, 714, 348, 188, 370, 619, 458, 981, 630, 70, 998, 753, 913, 250, 743, 536, 733, 960, 374, 978, 943, 194, 457, 101, 914, 174, 122, 582, 520, 96, 715, 28, 165, 0, 192, 946, 90, 870, 849, 257, 703, 152, 475, 226, 878, 306, 285, 721, 266, 888, 436, 558, 214, 17]

# Draw plot

# Find out similar labels
# by ranking correlation of the slices
from scipy.stats import spearmanr as tau

sims = {}
for layer in layernames:
    sims[layer] = {}
    for label0 in range(10):
        for label1 in range(10):
            if (label0, label1) in sims[layer]:
                continue
            if (label1, label0) in sims[layer]:
                continue
            if label0 == label1:
                continue
            rank0 = cell_sort[label0][layer]
            rank1 = cell_sort[label1][layer]
            corr, p = tau(rank0, rank1)
            sims[layer][(label0, label1)] = corr

# Plot the similarity by confusion matrix
from sklearn.metrics import confusion_matrix
import matplotlib.pyplot as plt

for layer in layernames:
    print('      -------- {} -------'.format(layer))
    sorted_pairs = sorted(sims[layer], key=sims[layer].get)
    for th, pair in enumerate(sorted_pairs):
        if th % 10 == 0:
            print('\n      ', pair, end="\t")
        else:
            print(pair, end="\t")
    print('\n')

      -------- conv1 -------

       (0, 7)	(1, 3)	(1, 7)	(5, 7)	(3, 7)	(1, 6)	(2, 7)	(1, 2)	(0, 6)	(1, 9)	
       (5, 6)	(3, 9)	(3, 6)	(1, 5)	(1, 4)	(0, 3)	(2, 6)	(7, 8)	(4, 7)	(3, 8)	
       (1, 8)	(7, 9)	(3, 4)	(0, 9)	(0, 1)	(0, 8)	(5, 9)	(0, 4)	(2, 9)	(0, 2)	
       (5, 8)	(0, 5)	(4, 6)	(4, 5)	(3, 5)	(6, 8)	(2, 8)	(6, 9)	(2, 4)	(2, 3)	
       (6, 7)	(4, 9)	(8, 9)	(2, 5)	(4, 8)	

      -------- pool1 -------

       (1, 3)	(1, 6)	(1, 2)	(5, 7)	(3, 7)	(2, 7)	(3, 4)	(2, 4)	(3, 9)	(4, 5)	
       (1, 5)	(0, 1)	(6, 7)	(3, 8)	(0, 3)	(4, 6)	(7, 8)	(5, 9)	(6, 9)	(6, 8)	
       (1, 8)	(0, 7)	(4, 8)	(2, 9)	(0, 4)	(0, 6)	(3, 6)	(1, 9)	(8, 9)	(2, 8)	
       (5, 6)	(7, 9)	(0, 2)	(1, 4)	(2, 3)	(4, 9)	(1, 7)	(5, 8)	(0, 5)	(0, 9)	
       (2, 6)	(3, 5)	(0, 8)	(2, 5)	(4, 7)	

      -------- conv2 -------

       (1, 5)	(0, 8)	(0, 5)	(5, 6)	(2, 6)	(1, 8)	(5, 9)	(3, 7)	(2, 7)	(2, 8)	
       (1, 7)	(3, 6)	(0, 7)	(3, 9)	(1, 4)	(3, 4)	(1, 2)	(4, 8)	(0, 2)	(0, 6)	
       (1, 6)	(0, 9)	(4, 5)	(1, 3)	(7, 9)	(7, 8)	(0, 1)	(2, 3)	(6, 7)	(3, 5)	
       (4, 7)	(5, 8)	(3, 8)	(5, 7)	(0, 4)	(2, 9)	(1, 9)	(2, 4)	(8, 9)	(0, 3)	
       (4, 6)	(2, 5)	(6, 8)	(4, 9)	(6, 9)	

      -------- pool2 -------

       (1, 4)	(2, 9)	(1, 5)	(1, 9)	(1, 2)	(0, 9)	(1, 8)	(0, 7)	(0, 4)	(3, 9)	
       (1, 3)	(1, 6)	(1, 7)	(3, 5)	(3, 6)	(2, 5)	(7, 8)	(2, 6)	(5, 7)	(0, 1)	
       (6, 7)	(0, 8)	(5, 6)	(2, 4)	(4, 7)	(8, 9)	(3, 4)	(6, 8)	(4, 6)	(7, 9)	
       (4, 9)	(6, 9)	(0, 2)	(5, 8)	(2, 7)	(3, 8)	(0, 3)	(0, 5)	(0, 6)	(5, 9)	
       (4, 5)	(2, 3)	(4, 8)	(3, 7)	(2, 8)	

      -------- fc1 -------

       (1, 2)	(0, 1)	(7, 8)	(1, 5)	(1, 8)	(1, 7)	(0, 9)	(2, 3)	(2, 5)	(5, 6)	
       (1, 6)	(1, 4)	(4, 8)	(3, 5)	(0, 8)	(0, 3)	(2, 7)	(6, 8)	(0, 4)	(5, 9)	
       (1, 3)	(4, 5)	(3, 8)	(0, 5)	(8, 9)	(3, 6)	(2, 4)	(1, 9)	(4, 6)	(2, 9)	
       (6, 7)	(3, 4)	(5, 8)	(0, 2)	(5, 7)	(2, 8)	(7, 9)	(3, 7)	(4, 9)	(6, 9)	
       (4, 7)	(0, 7)	(3, 9)	(2, 6)	(0, 6)